Herding, Heterogeneity, and Momentum Trading of Institutional Investors Across Asset Classes
Bibliographic record
Abstract
This paper examines herding, heterogeneity, and momentum trading of institutional investors in Israel across a broad variety of financial assets. While previous studies typically focus on stocks only, we examine herding patterns, heterogeneity, and momentum trading of institutional investors in five asset classes. We find that during the sample period (1/2002 – 12/2011) large investors tended to herd more than medium and small-size investors. In contrast, small investors used momentum trading patterns more than medium and large-size investors. Homogeneity was found among large investors, especially pension funds, and during the first half of the 2000s, when investors purchased corporate bonds at the expense of government bonds. This phenomenon ended upon the beginning of the subprime crisis and against the backdrop of the financial difficulties of the bond issuers. In those years, panicked investors withdrew funds from the most liquid institutions (study funds), while infusing funds to pension and provident funds due to legally binding arrangements. We attribute some of the heterogeneous trading of the institutional investors, to those factors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".